Adaptive Learned Image Compression with Graph Neural Networks

Efficient image compression relies on the accurate detection and elimination of both local and global redundancy. While most state-of-the-art (SOTA) learned image compression (LIC) methods are built on Convolutional Neural Networks (CNNs) or Transformer architectures, these frameworks are inherently rigid. Standard CNN kernels and window-based attention mechanisms impose fixed receptive fields and static connectivity patterns, which potentially couple non-redundant pixels simply due to their proximity in Euclidean space. This rigidity limits the model’s ability to adaptively capture spatially varying redundancy across the image, particularly at the global level.To overcome these limitations, we propose a content-adaptive image compression framework based on Graph Neural Networks (GNNs). Specifically, our approach constructs dual-scale graphs that enable flexible, data-driven receptive fields. Furthermore, we introduce adaptive connectivity by dynamically adjusting the number of neighbors for each node based on local content complexity. These innovations empower our Graph-based Learned Image Compression (GLIC) model to effectively model diverse redundancy patterns across images, leading to more efficient and adaptive compression.Experiments demonstrate that GLIC achieves SOTA performance, outperforming VTM-9.1 by-19.29%, -21.69%, -18.71% in BD-rate on Kodak, Tecnick, and CLIC datasets, respectively. Code will be released.